{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy.stats import pearsonr\n",
    "from adjustText import adjust_text\n",
    "from scipy import stats\n",
    "from mpl_toolkits.axes_grid1.inset_locator import mark_inset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pop_df=pd.read_csv(r'C:\\Users\\Yasaman\\Downloads\\World_bank_population.csv',skiprows=3)\n",
    "pop_df=pop_df[['Country Code','2003','2019']].dropna()\n",
    "pop_df['2019']=pop_df['2019'].astype(int)\n",
    "possible_countries=pop_df.query(\" `2019` >=1000000\")['Country Code'].values\n",
    "possible_countries=[x.lower() for x in possible_countries]\n",
    "\n",
    "\n",
    "excluded_iso3_codes = [\n",
    "    \"IRL\",  # Ireland\n",
    "    \"SSD\",  # South Sudan\n",
    "    \"SDN\",  # Sudan\n",
    "    \"COG\",  # Republic of the Congo\n",
    "    \"COD\",  # Democratic Republic of the Congo\n",
    "    \"GIN\",  # Guinea\n",
    "    \"GNB\",  # Guinea-Bissau\n",
    "    \"GNQ\",  # Equatorial Guinea\n",
    "    \"PNG\",  # Papua New Guinea\n",
    "    \"XKX\",  # Kosovo (unofficial)\n",
    "    \"MNE\",  # Montenegro\n",
    "    \"SRB\",  # Serbia\n",
    "    \"TLS\"   # Timor-Leste\n",
    "]\n",
    "excluded_iso3_codes=[c.lower() for c in excluded_iso3_codes]\n",
    "\n",
    "possible_iso=list(set(possible_countries)-set(excluded_iso3_codes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "physical_sciences=[ 'MATH', 'ENGI', 'PHYS', 'COMP','MULT']\n",
    "df=pd.read_csv(r\"C:\\Users\\Yasaman\\Downloads\\Mentioned-Funding-fractional counting.csv\")\n",
    "df=df[~(df['subjarea'].isin(physical_sciences))]\n",
    "df=df.groupby(['year', 'country', 'funding_country','subjarea']).sum().reset_index()\n",
    "\n",
    "df=df[(df['year'].isin(np.arange(2002,2020,1)))&(df['funding_country'].isin(possible_iso)) &(df['country'].isin(possible_iso))].reset_index(drop=True)\n",
    "df['is_internal']=df['funding_country']==df['country']\n",
    "df['year_arabspring']=df['year'].apply(lambda x: 'before' if x>=2002 and x<=2010 else 'after' )\n",
    "df=df.rename(columns={'year':'Year', 'aggregated_value':'count', 'country':'Mention_country', 'funding_country':'Aff_country'})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "Country_list={'Egypt':'EGY', 'Tunisia':'TUN','Libya':'LBY','Syria':'SYR','Yemen':'YEM','Bahrain':'BHR','Jordan':'JOR','Kuwait':'KWT','Morocco':'MAR','Oman':'OMN'}\n",
    "rev_Country_list={Country_list[key]: key for key in Country_list}\n",
    "abbr=[country.lower() for country in Country_list.values()]\n",
    "country_codes=pd.read_csv(r\"C:\\Users\\Yasaman\\Downloads\\iso3.csv\")\n",
    "country_codes['iso3']=[c.lower() for c in country_codes['iso3']]\n",
    "physical_sciences=[ 'MATH', 'ENGI', 'PHYS', 'COMP', \"MUL\"]\n",
    "map={country_codes.iloc[c]['iso3']: country_codes.iloc[c]['name'] for c in range(len(country_codes))}\n",
    "map['irn']='Iran'\n",
    "map['usa']='USA'\n",
    "map['gbr']='UK'\n",
    "filtered_df=df[(df['Mention_country'].isin(abbr))&(df['Mention_country']!=df['Aff_country'])]\n",
    "\n",
    "before_df=filtered_df[filtered_df['Year'].isin(np.arange(2002, 2011, 1))].groupby(by=['Mention_country','Aff_country']).sum().reset_index()[['count','Mention_country','Aff_country']].rename(columns={'count':'count_before'})\n",
    "after_df=filtered_df[filtered_df['Year'].isin(np.arange(2011, 2020, 1))].groupby(by=['Mention_country','Aff_country']).sum().reset_index()[['count','Mention_country','Aff_country']].rename(columns={'count':'count_after'})\n",
    "compare_df=before_df.merge(after_df, how='outer', on=['Mention_country','Aff_country']).fillna(0)\n",
    "compare_df['count_after']/=9\n",
    "compare_df['count_before']/=9\n",
    "compare_df=compare_df.groupby(['Aff_country'])[['count_before', 'count_after']].sum().reset_index()\n",
    "compare_df['difference']=compare_df['count_after']-compare_df['count_before']\n",
    "compare_df=compare_df.sort_values('difference', ascending=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_df=pd.read_csv(r\"C:\\Users\\Yasaman\\Downloads\\World_bank_GDP_per_capita.csv\",skiprows=4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Yasaman\\AppData\\Local\\Temp\\ipykernel_23692\\2056405492.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  selected_df['average_gdp_pc'] = selected_df[average_columns].mean(axis=1, skipna=True)\n"
     ]
    }
   ],
   "source": [
    "columns =[country_df.columns[1]]+list(np.arange(2002, 2020, 1).astype(str))   # Convert year numbers to strings if columns are named as strings\n",
    "selected_df = country_df[columns]  # Select the columns\n",
    "# Generate the list of column names\n",
    "average_columns = np.arange(2002, 2020, 1).astype(str)\n",
    "\n",
    "# Calculate the row-wise average, ignoring NaN values\n",
    "selected_df['average_gdp_pc'] = selected_df[average_columns].mean(axis=1, skipna=True)\n",
    "\n",
    "\n",
    "\n",
    "selected_df=selected_df[[country_df.columns[1],'average_gdp_pc']]\n",
    "selected_df['Country Code']=selected_df['Country Code'].apply(lambda x:x.lower())\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "country_df=selected_df.merge(compare_df, left_on='Country Code', right_on='Aff_country', how='left').dropna(subset=['Aff_country'])\n",
    "country_df=country_df[country_df['Aff_country'].isin(possible_countries)]\n",
    "\n",
    "\n",
    "country_codes=pd.read_csv(r\"C:\\Users\\Yasaman\\Downloads\\iso3.csv\")\n",
    "country_codes['iso3']=[c.lower() for c in country_codes['iso3']]\n",
    "map={country_codes.iloc[c]['iso3']: country_codes.iloc[c]['name'] for c in range(len(country_codes))}\n",
    "map['irn']='Iran'\n",
    "map['usa']='USA'\n",
    "map['gbr']='UK'\n",
    "map['rus']='Russia'\n",
    "map['syr']='Syria'\n",
    "map['are']='UAE'\n",
    "\n",
    "\n",
    "plot_a_df=country_df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "are\n",
      "aus\n",
      "aut\n",
      "bel\n",
      "can\n",
      "che\n",
      "chn\n",
      "deu\n",
      "dnk\n",
      "esp\n",
      "fin\n",
      "fra\n",
      "gbr\n",
      "jpn\n",
      "lby\n",
      "nld\n",
      "nor\n",
      "qat\n",
      "sau\n",
      "sgp\n",
      "swe\n",
      "usa\n",
      "Pearson Correlation Coefficient: 0.38315596239210215\n",
      "P-value: 0.0001268779902940255\n",
      "Spearman Correlation Coefficient: 0.5565128535470397\n",
      "P-value: 4.74747702606667e-09\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1=plt.subplots(nrows=1, ncols=1)\n",
    "ins = ax1.inset_axes([0.08,0.61,0.45,0.35])\n",
    "\n",
    "\n",
    "sns.regplot(plot_a_df, x='average_gdp_pc', y='difference' ,marker=\"x\", color=\".2\", line_kws=dict(color=\"#7DE1DF\"),ax=ax1,  scatter_kws={\"zorder\":10}, robust=True)\n",
    "ax1.set_ylabel(r'$\\Delta F_i^*$', fontsize=18)\n",
    "ax1.set_xlabel('Average GDP per capita', fontsize=18)\n",
    "\n",
    "\n",
    "sns.regplot(plot_a_df, x='average_gdp_pc', y='difference' ,marker=\"x\", color=\".2\", line_kws=dict(color=\"#7DE1DF\"),ax=ins,  scatter_kws={\"zorder\":10}, robust=True)\n",
    "ins.set_ylabel(r'', fontsize=10)\n",
    "ins.set_xlabel(r'', fontsize=10)\n",
    "ins.set_xlim(0, 10000)\n",
    "ins.set_ylim(-35/8, 10)\n",
    "\n",
    "\n",
    "points=[]\n",
    "# Annotate each point\n",
    "for line in plot_a_df[(plot_a_df['difference']>10)|(plot_a_df['average_gdp_pc']>40000)].index:\n",
    "\n",
    "    x = plot_a_df.average_gdp_pc[line]\n",
    "    y = plot_a_df.difference[line]\n",
    "    label =map[plot_a_df.Aff_country[line]]\n",
    "    print(plot_a_df.Aff_country[line])\n",
    "    points+=[ax1.text(x, y, label,\n",
    "                    fontsize=8, ha='center', va='center')]\n",
    "\n",
    "adjust_text(points, arrowprops=dict(arrowstyle=\"-\", color='k', lw=0.5, alpha=.5), expand=(1.5, 2.5))        \n",
    "\n",
    "points2=[]\n",
    "# Annotate each point\n",
    "for line in plot_a_df[(plot_a_df['difference']<=12)& (plot_a_df['difference']>4)&(plot_a_df['average_gdp_pc']<10000)].index:\n",
    "\n",
    "    x = plot_a_df.average_gdp_pc[line]\n",
    "    y = plot_a_df.difference[line]\n",
    "    label =map[plot_a_df.Aff_country[line]]\n",
    "    points2+=[ins.text(x, y, label,\n",
    "                    fontsize=8, ha='center', va='center')]\n",
    "    \n",
    "adjust_text(points2, arrowprops=dict(arrowstyle=\"-\", color='k', lw=0.5, alpha=.5), expand=(1.5, 2), ax=ins)        \n",
    "\n",
    "\n",
    "\n",
    "correlation_coefficient, p_value = pearsonr(plot_a_df['difference'], plot_a_df['average_gdp_pc'])\n",
    "print(\"Pearson Correlation Coefficient:\", correlation_coefficient)\n",
    "print(\"P-value:\", p_value)\n",
    "\n",
    "correlation_coefficient, p_value = stats.spearmanr(plot_a_df['difference'], plot_a_df['average_gdp_pc'])\n",
    "print(\"Spearman Correlation Coefficient:\", correlation_coefficient)\n",
    "print(\"P-value:\", p_value)\n",
    "\n",
    "mark_inset(ax1, ins, loc1=2, loc2=4, fc=\"none\", ec=\"0.5\", linestyle=':')\n",
    "\n",
    "fig.savefig('funding_vs_gdp.pdf', bbox_inches='tight')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
